Extremis turns every decision, incident, and conversation across Slack, Notion, GitHub, Linear, Jira, Asana, and Fathom into a living company memory — and surfaces it the moment someone joins, leaves, or relitigates a decision you already made.
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Workflows
Not another search box. Opinionated playbooks for the situations where institutional knowledge actually decides whether a week is smooth or on fire.
Generate their map of the codebase on day one — the repos that matter, the PRs to read, the incidents to know, the people to meet.
Sample output
See what's about to break. Which systems lose their only expert. Which decisions never got documented. Which knowledge walks out the door.
Sample output
Replay the why. The Slack thread, the linked PR, the Fathom meeting clip, the Jira ticket — every fragment of the original decision, in order.
Sample output
Same symptom, same fix. Pulls the prior post-mortem, the playbook, and the engineer who solved it last time — before it becomes a 4-hour outage.
Sample output
Workflows ship on top of the same connectors + retrieval layer you already get on day one. See the architecture →
Connects to the tools your team and agents already use
The hidden tax
The thing you're looking for already exists. It's in another tool — or in someone's head — or in a thread from eight months ago. Extremis stops the hunt.
The answer to ‘why did we deprecate X?’ is buried in a Slack thread from 8 months ago.
The follow-up you need to send is three calls and a Slack reply deep.
The customer's last issue is six tickets and a Slack DM away.
Your spec was accurate the day it was written. That was a year ago.
New hires ask the same five questions every quarter. Nobody indexes the answers.
“Didn't we already decide this?” — yes, in a 47-minute call, three months ago.
You're shipping a feature that touches the same code path as last year's 4-hour outage. The Jira post-mortem exists — if anyone remembers to look.
What people ask
Ask in the dashboard, ask in Slack via /extremis, or ask from inside Claude / Cursor via the MCP server. Same memory, same cited answers, wherever you work.
StarkSphereLabs
Marcus Vega
Customer pipeline · pricing intel · Q3 push
/extremis what did Acme say about pricing on the Q3 call?
Acme pushed back on the $499/seat tier — they want volume discounts above 50 seats 1. Their two unresolved blockers were enterprise SSO 2 and EU data residency 3.
Acme — Q3 negotiation call
fathom · 47m · jump to 23:14
Acme pricing pushback
#deals · 12 messages · Friday 4:30 PM
EU residency requirement
enterprise/acme · pinned by Alice
perfect, thx — pinging legal about residency today 👀
A handful more questions Extremis has answered in production:
Lena Park · Eng Lead
Engineering“Who has the most context on PR #1284?”
github · slack #auth · 3 sources
Marcus Vega · AE
Sales“What did Acme say about pricing on the Q3 call?”
fathom · slack #deals · 4 sources
Priya Shah · PM
Product“What's the latest status on billing-v2?”
linear · notion · 5 sources
Jamal Reid · Support
Support“What's our refund policy for annual churn at month 2?”
notion · slack #ops · 2 sources
Ari Cohen · Founder
Personal“What did Claude say about our pricing tier strategy?”
fathom · slack DM · 3 sources
Diego Romero · Ops
Operations“Why did we drop SSE for WebSockets in March?”
slack #infra · linear ENG-247 · 3 sources
Record anywhere · Recall everywhere
Same memory, four surfaces. Record something in a Fathom call; recall it in Claude. Ask in Slack; the same answer is in the dashboard. Living playbooks your team reads on Monday. Context Claude reaches for on Tuesday.
/extremis what did Acme decide on pricing?
Built-in MCP — recall is available as a tool to any agent
Search box at /dashboard/memories — RAG-style cited answers
mem.remember() / mem.recall() — 2-line Python or TypeScript client
What makes Extremis different
Most “company brain” tools stop at sync + embed. Extremis adds verification, debuggable ranking, public benchmarks, and an MIT-licensed exit door.
Every memory passes a two-tier verifier at write time — NLI model first, LLM judge for grey-zone scores. Failing memories aren't dropped silently — they're tagged unverified and down-ranked at recall.
Every recall returns a one-line reason — “similarity 0.87 · score +2.0 · used 5× · 3d old”. Debuggable retrieval, not a black box. Asymmetric 1.5× weight on negative outcomes.
Reproducible on the public benchmark · 500 QA instances · ~53 sessions each. p50 recall latency ~35ms once HNSW indexes warm up.
Cloud is convenience, not a moat. The memory library is open source. If Cloud goes away, point HostedClient.base_url at your own deployment — nothing else in your code changes.
Want the data in your own VPC? Run your own Postgres + pgvector — Extremis hosts the workers + sync engine, your storage stays put.
Each customer is a Postgres row-level-secured namespace. Cross-tenant leak is impossible at the database layer, not just at the application.
Who it's for
For teams
The why behind the what — every decision tagged with the conversation it came from. The meeting that ends the meeting.
For individuals + agent builders
Want a wiki of your own — your notes, your decisions, your context, all in one place and all queryable from any agent? Extremis works for one person too. Real memory, built from your own work, available wherever you build.
mem.remember() + mem.recall() — two linesPricing
You only pay for usage costs (OpenAI embeddings + LLM calls). Extremis Cloud itself is free for early adopters — and stays free on the Starter tier forever.
Free
for early adopters · forever
$49
/mo per workspace
Custom
contact sales
Stop hunting through Slack, Notion, GitHub, and Jira. Connect once, search forever.
~5 minute setup · No credit card · Free during beta